AI Adoption Soars: Embeddable AI Revolutionizes Applications
Artificial Intelligence (AI) adoption is on the rise, as companies embrace an AI-plus mindset to fuel business growth. One concept gaining traction is Embeddable AI, which enables the infusion of AI into the core of solutions, making them more intelligent, efficient, intuitive, and automated.
Developers can seamlessly integrate flexible, enterprise-grade AI capabilities into their applications through Embeddable AI. This is made possible by containerized libraries, which are built on an open-source framework and offer pre-trained models. These libraries significantly reduce the time and resources required to add AI to applications.
Containerized libraries are not only efficient but also versatile. They can run anywhere, are flexible and extensible, and can help reduce infrastructure costs due to their lightweight nature. This makes them an ideal choice for developers looking to enhance their applications with AI capabilities.
In a video by IBM, Martin Keen discusses AI deployment, specifically focusing on two major methods: containerized libraries and applications. Applications are a distinct type of software designed to execute specific tasks or deliver specified functionality to end users. They offer various advantages, such as low or no-code options that make them more accessible to users without coding knowledge. This enables easy interaction and engagement without the need to learn coding.
Applications also provide a swift route to market, enhancing efficiency in business processes. They can be rapidly delivered to users, making them ideal for organizations incorporating new technologies and capabilities. Moreover, applications reduce development costs by allowing certain developmental aspects to be handled by the application creating company, saving time and resources.
Incorporating AI into applications is a significant step that requires responsible, trustworthy, and secure management. Responsible AI emphasizes governance structures that outline rules, policies, and accountability throughout the AI lifecycle. This ensures ethically sound AI deployment that complies with necessary standards and regulations.
Trustworthy AI focuses on using unbiased, domain-specific expertise to develop training data, resulting in reliable and accurate AI. Additionally, Secure AI ensures round-the-clock enterprise-grade support and security to mitigate potential disruptions or issues impacting AI operation.
The choice between using a library or an application to integrate AI depends on various factors, including the solution’s environment, compute costs, and go-to-market strategy. Both options possess unique strengths in terms of flexibility, security, and reliability. Therefore, selecting the right approach depends on the specific needs and requirements of the company to ensure optimal usage and effectiveness of AI implementation.
As the world embraces the AI-plus mindset, the importance of Embeddable AI will continue to grow. Understanding needs and leveraging the strengths of each form factor is essential for successful AI deployment, driving innovation and unlocking the full potential of AI-powered solutions.
